Differential Gene Expression Analysis
Here, we’ll build our helper functions for running the differential gene expression analysis.
updown_genes <- function(expr.lst, de.genes){
### This function will return two lists of up/down regulated
### genes when a cell line is sensitive to each drug.
### One list will contain genes that
### are up regulated in the lower responding cell lines,
### and one in the lower responding lines.
### The function will take a matrix (expr.lst$x) containing
### the expression values for the cell lines pertaining
### to the DE expression analysis being run. The cell lines (columns)
### correspond to the class labels found in expr.lst$y.
### Cell lines labeled '1' have a high AUC to the drug in
### question and cell lines labeled '2' have a low AUC
### to the drug in question.
### First, the matrix will be separated into high/low AUC
### cell lines. Then, a loop will be run with the genes that are
### given as differentially expressed. The genes with a higher
### average in the high AUC cell lines will be labeled as genes
### whose expression is increased. Genes with a lower average in the
### high AUC cell lines will be labeled as genes whose expression is
### decreased.
data.mat <- expr.lst$x # Expression values
data.class <- expr.lst$y # Class values
res.cells_lines <- data.mat[ , which(data.class == 1)] # Resistant, high AUC
sens.cell_lines <- data.mat[ , which(data.class == 2)] # Sensitive, low AUC
up.genes <- c() # Up in resistant
down.genes <- c() # Down in resistant
extra.genes <- c()
for(gene in de.genes){
mean.res <- rowMeans(res.cells_lines[gene, ]) # Mean expr in resistant
mean.sens <- rowMeans(sens.cell_lines[gene, ]) # Mean expr in sensitive
if (mean.sens > mean.res) { # Sensitive expr > Resistant expr?
up.genes <- c(up.genes, gene) # High expr in sens = up-regulated
} else if (mean.sens < mean.res) { # Sensitive expr < Resistant expr?
down.genes <- c(down.genes, gene) # Low expr in sens = down-regulated
} else {
extra.genes <- c(extra.genes, gene) #
}
}
return(list(up = up.genes, down = down.genes, extra = extra.genes))
}
high_low_subset <- function(drug_data, drug, percent){
low_quant <- quantile(drug_data[ , drug], probs = percent, na.rm = TRUE)
high_quant <- quantile(drug_data[ , drug], probs = 1-percent, na.rm = TRUE)
median_EC50 <- median(drug_data[ , drug])
high_lines <- row.names(drug_data[drug_data[ , drug] > high_quant , ])
low_lines <- row.names(drug_data[drug_data[ , drug] < low_quant , ])
return(list(high_lines = high_lines,
low_lines = low_lines))
}
input_DE <- function(drug_data, expr_data, meta_df, subset_cat = NULL,
group_value = NULL, drug, dr_type, cutoff){
# This is a crucial function for creating the input to the user-defined DE fns
# below.
# First it subsets the data if a subset category and group value are provided.
# Next, it finds the names of high/low cell lines based on the drug data,
# speficied drug, and percent of top/bottom responders that we are comparing.
# Then, it extracts the drug data for the given cell line names and labels them
# as 1 for high drug response metric or 2 for low drug response metric.
#
# A list is return containing the following:
# x: drug response data for the combined high and low cell lines
# y: label, 1 or 2, categorizing x's cell lines as high or low responders
# num.high: length of high cell lines
# num.low: length of low cell lines # probably not necessary, will likely remove later
# if(is.null(group.value) == FALSE){
# drug.data <- subset.df(meta.df = meta.df,
# data.df = drug.data,
# meta.ID.index = 2,
# data.ID.index = "row",
# column = subset.cat,
# group.value = group.value)}
high_low_lines <- high_low_subset(drug_data, drug, percent = cutoff)
high_lines <- high_low_lines$high_lines
low_lines <- high_low_lines$low_lines
highresp_expr <- expr_data[ , colnames(expr_data) %in% high_lines]
lowresp_expr <- expr_data[ , colnames(expr_data) %in% low_lines]
x <- cbind(highresp_expr, lowresp_expr)
y <- c(rep(2, ncol(highresp_expr)), rep(1, ncol(lowresp_expr)))
return(list(x = x, y = y,
num_high = ncol(highresp_expr),
num_low = ncol(lowresp_expr)))
}
updown_genes <- function(expr.lst, de.genes){
### This function will return two lists of up/down regulated
### genes when a cell line is sensitive to each drug.
### One list will contain genes that
### are up regulated in the lower responding cell lines,
### and one in the lower responding lines.
### The function will take a matrix (expr.lst$x) containing
### the expression values for the cell lines pertaining
### to the DE expression analysis being run. The cell lines (columns)
### correspond to the class labels found in expr.lst$y.
### Cell lines labeled '1' have a high AUC to the drug in
### question and cell lines labeled '2' have a low AUC
### to the drug in question.
### First, the matrix will be separated into high/low AUC
### cell lines. Then, a loop will be run with the genes that are
### given as differentially expressed. The genes with a higher
### average in the high AUC cell lines will be labeled as genes
### whose expression is increased. Genes with a lower average in the
### high AUC cell lines will be labeled as genes whose expression is
### decreased.
data.mat <- expr.lst$x # Expression values
data.class <- expr.lst$y # Class values
res.cells_lines <- data.mat[ , which(data.class == 2)] # Resistant, high AUC
sens.cell_lines <- data.mat[ , which(data.class == 1)] # Sensitive, low AUC
up.genes <- c() # Up in resistant
down.genes <- c() # Down in resistant
extra.genes <- c()
for(gene in de.genes){
mean.res <- rowMeans(res.cells_lines[gene, ]) # Mean expr in resistant
mean.sens <- rowMeans(sens.cell_lines[gene, ]) # Mean expr in sensitive
if (mean.res > mean.sens) { # Resistant expr > sensitive expr?
up.genes <- c(up.genes, gene) # High expr in resistant = up-regulated
} else if (mean.res < mean.sens) { # Resistant expr < sensitive expr?
down.genes <- c(down.genes, gene) # Low expr in resistant = down-regulated
} else {
extra.genes <- c(extra.genes, gene) #
}
}
return(list(up = up.genes, down = down.genes, extra = extra.genes))
}
EBSeq_DE <- function(expr_lst){
data_mat <- as.matrix(expr_lst$x) # Expression values
data_class <- factor(expr_lst$y) # Class values
EBOut <- EBTest(Data=data_mat,
Conditions=data_class,
sizeFactors=MedianNorm(data_mat),
maxround = 15)
return(EBOut)
}
run_de_analysis <- function(drug_data, expr_data, drug, group_perc, FDR){
input_df <- input_DE(drug_data = drug_data,
expr_data = expr_data,
drug = drug,
cutoff = group_perc)
de_all <- EBSeq_DE(input_df)
de_signif <- GetDEResults(de_all, FDR = FDR)
updown_de <- updown_genes(input_df, de_signif$DEfound)
write.table(updown_de$up,
file = paste0("../DE_Results/DE_up_resistant_", drug, ".csv"),
sep = ",",
quote = FALSE, row.names = FALSE, col.names = FALSE)
write.table(updown_de$down,
file = paste0("../DE_Results/DE_up_sensitive_", drug, ".csv"),
sep = ",",
quote = FALSE, row.names = FALSE, col.names = FALSE)
}
And finally, we’ll run our differential expression analysis using EBSeq between the top/bottom 1/3 of responders to each drug.
for (drug_name in colnames(EC50_change_data_seq)){
EC50_change_data_seq["I1", "Vin"] <- NA
print(drug_name)
write(drug_name, file = "../DE_Results/all_DE_results_latex.txt", append = TRUE)
run_de_analysis(drug_data = EC50_change_data_seq,
expr_data = expr_data_lab,
drug = drug_name,
group_perc = 0.3333,
FDR = 0.05)
resistant_genes <- tryCatch(read.table(file =
paste0("../DE_Results/DE_up_resistant_", drug_name, ".csv")),
error = function(e) {table("No Differentially Expressed Genes Found")})
sensitive_genes <- tryCatch(read.table(file =
paste0("../DE_Results/DE_up_sensitive_", drug_name, ".csv")),
error = function(e) {table("No Differentially Expressed Genes Found")})
write(paste("Genes Up-Regulated in", drug_name, "Resistant State:"),
file = "../DE_Results/all_DE_results_latex.txt", append = TRUE)
print(xtable(resistant_genes),
file = "../DE_Results/all_DE_results_latex.txt", append = TRUE)
write(paste("Genes Up-Regulated in", drug_name, "Sensitive State:"),
file = "../DE_Results/all_DE_results_latex.txt", append = TRUE)
print(xtable(sensitive_genes),
file = "../DE_Results/all_DE_results_latex.txt", append = TRUE)
print("Up in resistance: ")
print(xtable(resistant_genes, caption = paste("Genes with increased expression in ",
drug_name, "resistance")), type = "html")
print("Up in sensitivity: ")
print(xtable(sensitive_genes, caption = paste("Genes with increased expression in ",
drug_name, "sensitivity")), type = "html")
}
[1] “ActD” Removing transcripts with 100 th quantile < = 0 20962 transcripts will be tested [1] “Up in resistance:”
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V1
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1
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SNORA24B
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2
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SNORA27
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3
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SNORD116-14
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[1] “Up in sensitivity:”
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V1
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1
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CLN8
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2
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DENND4B
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3
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G6PD
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4
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IKBKB
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5
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MEG8
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6
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NRG1-IT1
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7
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NSUN5P1
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8
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PARVG
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9
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SYNGAP1
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10
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ZNF582-AS1
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[1] “Cyclo” Removing transcripts with 100 th quantile < = 0 20497 transcripts will be tested [1] “Up in resistance:”
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V1
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1
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ALX1
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2
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AMPD2
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3
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ANKRD29
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4
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ANO5
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5
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ARID1B
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6
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ATF5
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7
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BAHCC1
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8
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BBS1
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9
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BCR
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10
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BOLA3
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11
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BUD31
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12
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C18orf32
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13
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CAD
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14
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CAMTA1
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CDC42SE2
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CLCN6
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COL4A2
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CYB561
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DCN
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DDIT4
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DDX11
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22
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DGCR8
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23
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DHCR7
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24
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DYNC1H1
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25
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FABP5P7
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26
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FAM102B
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27
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FAM72D
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28
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FLNA
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29
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FLRT2
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30
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G6PD
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31
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GCN1
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32
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GNAI1
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33
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GNG10
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34
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GTF2A2
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35
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HDAC10
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36
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HIST1H1C
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37
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HIST1H1T
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38
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HMGN3
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39
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HOXB7
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40
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HSPA2
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41
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HYOU1
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42
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INTS1
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43
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KIF1A
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44
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KIFC3
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45
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LDLR
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46
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LENG8
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47
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LINC01089
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48
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LITAF
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49
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LRCH4
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50
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LSS
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51
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MAN2C1
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52
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MTMR4
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53
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NAA20
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54
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NDUFA5
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55
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NEBL
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56
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NSUN5P1
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57
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ORMDL3
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58
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PCDH17
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59
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PCNX3
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60
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PCSK9
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61
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PDCD2L
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62
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PELP1
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63
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PIEZO1
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64
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PLXNA1
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65
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POLR2K
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66
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PRPF38B
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67
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PRRC2A
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68
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RALGDS
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69
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RIPPLY3
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70
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RNF141
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71
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RPS26P8
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72
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RTN1
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73
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RUNDC3B
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74
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RWDD1
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75
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SBNO2
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76
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SERPINH1
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77
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SGSM2
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78
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SLC12A7
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79
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SLC29A2
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80
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SLC39A8
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81
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SLC6A9
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82
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SPTAN1
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83
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SRRM2
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84
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SRSF11
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85
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SSTR1
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86
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SUCLG2
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87
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SUPT5H
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88
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TMEM60
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89
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TOP3B
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90
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TRAPPC9
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91
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TRIM16
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92
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TUBGCP6
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93
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VKORC1L1
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94
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ZNF654
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95
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ZNF76
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[1] “Up in sensitivity:”
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V1
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1
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ADGRL2
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2
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AHNAK
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3
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ALK
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4
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ANAPC2
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5
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ARHGEF9
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6
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BACE1-AS
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7
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C7
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8
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CAMKV
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9
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CCN1
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10
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CD101
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11
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CD24
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CD44
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CLASRP
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CNOT3
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15
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COL7A1
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16
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CRMP1
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CYFIP2
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CYP4F23P
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19
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DENND4B
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20
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DERL3
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21
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EFHC1
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22
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FAM27C
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FASN
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GUCY1A2
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HS6ST2
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JUN
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27
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KIAA0556
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KMT2B
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LINC01237
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30
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LRCH2
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31
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MALAT1
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32
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MCF2L
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MED12
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MEG3
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MEG8
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MRGPRF
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MVD
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MYH10
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NBEAL2
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40
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NEFM
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NUP210
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42
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PARVG
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PC
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44
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PLEC
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45
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PYGO1
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RASAL3
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RNA5SP284
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RNF130
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RNF44
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SNORA5C
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51
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SPIN4
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SYNGAP1
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TEPSIN
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TMEM271
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TSPAN2
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WDR27
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57
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ZNF239
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58
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ZNF667-AS1
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59
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ZNF736
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[1] “Doxo” Removing transcripts with 100 th quantile < = 0 20962 transcripts will be tested [1] “Up in resistance:”
[1] “Up in sensitivity:”
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V1
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1
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ARHGEF9
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2
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KSR1
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3
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LAMB3
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4
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PARVG
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[1] “Etp” Removing transcripts with 100 th quantile < = 0 21009 transcripts will be tested [1] “Up in resistance:”
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V1
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1
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ANKRD29
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2
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COPRS
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3
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DCN
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DUT
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5
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HMGN3
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6
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LLPH
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7
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LYN
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8
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MCTS2P
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9
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NDUFAF6
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10
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RPL21P39
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11
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RUNDC3B
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12
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SLC39A8
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13
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SSBP2
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14
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SSTR1
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15
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TOGARAM1
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16
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ZNF654
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[1] “Up in sensitivity:”
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V1
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1
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ABCC10
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2
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ADAM32
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3
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ARHGEF9
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4
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CAD
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5
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CD24
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6
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CD44
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7
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CDH4
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8
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CLASRP
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9
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CLU
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CNOT3
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11
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COL7A1
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12
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CORO7
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13
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CREB5
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14
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DENND4B
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15
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DHCR7
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16
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FASN
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G6PD
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GUCY1A2
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19
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HYOU1
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20
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IKBKB
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INTS1
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KATNB1
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23
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KCNH5
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24
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KSR1
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25
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LAMB3
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26
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LDLR
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27
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LENG8
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28
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MAN2C1
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MCF2L
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30
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MEG8
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MINK1
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32
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MRGPRF
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33
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MTHFR
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34
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NEBL
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35
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NEFM
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36
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NPDC1
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37
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NUCB1
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38
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PAX7
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39
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PC
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40
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PCSK7
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41
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PCSK9
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42
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PLXNB1
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43
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PRPF38B
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44
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PTX3
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45
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RAD9A
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46
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RHOD
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47
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SBNO2
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48
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SLC7A5
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49
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SVIL
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50
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SYNGAP1
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51
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TMEM94
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52
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TRAPPC9
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53
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TSPAN2
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54
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ZNF354C
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55
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ZNF582-AS1
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56
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ZNF736
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[1] “NaThio” Removing transcripts with 100 th quantile < = 0 21126 transcripts will be tested [1] “Up in resistance:”
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V1
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No Differentially Expressed Genes Found
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1
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[1] “Up in sensitivity:”
[1] “Ola” Removing transcripts with 100 th quantile < = 0 20941 transcripts will be tested [1] “Up in resistance:”
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V1
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1
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ANO10
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2
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FAM220A
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3
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HIST1H1T
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4
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HMGN3
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5
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HOXB7
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6
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PPP1R21
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7
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SAMD11
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8
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SNORA27
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9
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SSTR1
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10
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TAGLN
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[1] “Up in sensitivity:”
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V1
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1
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ADGRL2
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2
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AHNAK
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3
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ARHGEF28
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4
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ATG4B
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5
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CCL2
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6
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CCN1
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7
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CD24
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8
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CHD6
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9
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CLASRP
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10
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CLCN6
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11
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CLN8
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12
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DAB2
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13
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DENND4B
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14
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DHCR7
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15
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DYNC1H1
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16
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ERV3-1
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17
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FAM27C
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18
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FASN
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19
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HSPA2
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20
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IKBKB
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21
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JUN
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22
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KIFC3
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23
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LARGE2
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24
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LDLR
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25
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LENG8
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26
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LINC02104
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27
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LMF1
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28
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LTBP3
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29
|
MAN2C1
|
|
30
|
MCF2L
|
|
31
|
MED15
|
|
32
|
MEG8
|
|
33
|
MGAT5
|
|
34
|
MRGPRF
|
|
35
|
NEFM
|
|
36
|
NRG1-IT1
|
|
37
|
P2RY11
|
|
38
|
PC
|
|
39
|
PCSK7
|
|
40
|
PKN3
|
|
41
|
PLXNB1
|
|
42
|
PTX3
|
|
43
|
SIDT2
|
|
44
|
SLC6A9
|
|
45
|
SRRM2
|
|
46
|
SULT1B1
|
|
47
|
TECPR1
|
|
48
|
TMEM25
|
|
49
|
TMEM94
|
|
50
|
TRIM16L
|
|
51
|
TSPAN2
|
|
52
|
TTLL7
|
|
53
|
UNKL
|
[1] “Paz” Removing transcripts with 100 th quantile < = 0 21031 transcripts will be tested [1] “Up in resistance:”
[1] “Up in sensitivity:”
|
|
V1
|
|
No Differentially Expressed Genes Found
|
1
|
[1] “SAHA” Removing transcripts with 100 th quantile < = 0 20758 transcripts will be tested [1] “Up in resistance:”
|
|
V1
|
|
1
|
ABCB1
|
|
2
|
KAZALD1
|
|
3
|
RPS26
|
|
4
|
SMAD6
|
|
5
|
TRGC1
|
[1] “Up in sensitivity:”
|
|
V1
|
|
1
|
ACOT9
|
|
2
|
ACPP
|
|
3
|
AHR
|
|
4
|
B3GNT5
|
|
5
|
CCL2
|
|
6
|
FOS
|
|
7
|
GAL
|
|
8
|
NUP188
|
|
9
|
RN7SL5P
|
|
10
|
SCNN1G
|
|
11
|
TRAV5
|
[1] “SN38” Removing transcripts with 100 th quantile < = 0 21089 transcripts will be tested [1] “Up in resistance:”
|
|
V1
|
|
1
|
ANKRD29
|
|
2
|
ATP5MF
|
|
3
|
DCN
|
|
4
|
DUT
|
|
5
|
EXOSC3
|
|
6
|
FAM72D
|
|
7
|
HMGB1P10
|
|
8
|
HMGN3
|
|
9
|
LSM8
|
|
10
|
MED21
|
|
11
|
PSMG1
|
|
12
|
RBIS
|
|
13
|
RPL21
|
|
14
|
RUNDC3B
|
|
15
|
SLC39A8
|
|
16
|
SLIRP
|
|
17
|
SMR3B
|
|
18
|
SSBP2
|
|
19
|
SSTR1
|
|
20
|
WDR53
|
[1] “Up in sensitivity:”
|
|
V1
|
|
1
|
ABCC10
|
|
2
|
ADGRL2
|
|
3
|
ALK
|
|
4
|
AP5Z1
|
|
5
|
ARHGEF9
|
|
6
|
CAD
|
|
7
|
CCL2
|
|
8
|
CDH4
|
|
9
|
COL16A1
|
|
10
|
COL7A1
|
|
11
|
CORO7
|
|
12
|
CREB5
|
|
13
|
CRMP1
|
|
14
|
CYB561
|
|
15
|
DENND4B
|
|
16
|
DGCR8
|
|
17
|
DHCR7
|
|
18
|
EBI3
|
|
19
|
ECM1
|
|
20
|
FASN
|
|
21
|
FOS
|
|
22
|
G6PD
|
|
23
|
GPNMB
|
|
24
|
HSPA2
|
|
25
|
HYOU1
|
|
26
|
IKBKB
|
|
27
|
INTS1
|
|
28
|
KATNB1
|
|
29
|
KCNH5
|
|
30
|
LAMB3
|
|
31
|
LDLR
|
|
32
|
LENG8
|
|
33
|
LIPH
|
|
34
|
LOX
|
|
35
|
LUCAT1
|
|
36
|
MAN2C1
|
|
37
|
MAP1S
|
|
38
|
MEG8
|
|
39
|
MRGPRF
|
|
40
|
MTHFR
|
|
41
|
NEBL
|
|
42
|
NEFM
|
|
43
|
NFRKB
|
|
44
|
NLRC5
|
|
45
|
NPDC1
|
|
46
|
NPIPB3
|
|
47
|
NUCB1
|
|
48
|
PAX7
|
|
49
|
PCBP2-OT1
|
|
50
|
PCSK7
|
|
51
|
PCSK9
|
|
52
|
PIP5KL1
|
|
53
|
PLXNB1
|
|
54
|
PNPLA6
|
|
55
|
PRPF38B
|
|
56
|
PTOV1-AS2
|
|
57
|
PTX3
|
|
58
|
RNF44
|
|
59
|
SBNO2
|
|
60
|
SCNN1G
|
|
61
|
SLC7A5
|
|
62
|
SLITRK3
|
|
63
|
SUPT6H
|
|
64
|
SYNGAP1
|
|
65
|
SYT12
|
|
66
|
THY1
|
|
67
|
TRAPPC9
|
|
68
|
USP32P1
|
|
69
|
UTP3
|
|
70
|
ZNF354C
|
|
71
|
ZNF736
|
[1] “SP” Removing transcripts with 100 th quantile < = 0 20952 transcripts will be tested [1] “Up in resistance:”
|
|
V1
|
|
1
|
ADGRL2
|
|
2
|
ANKS6
|
|
3
|
AP3B2
|
|
4
|
AP5Z1
|
|
5
|
ARHGEF9
|
|
6
|
C7
|
|
7
|
CAMKV
|
|
8
|
CCAR2
|
|
9
|
CD24
|
|
10
|
CDH4
|
|
11
|
CHGA
|
|
12
|
CORO7
|
|
13
|
CRMP1
|
|
14
|
DGCR8
|
|
15
|
DHCR7
|
|
16
|
DPP3
|
|
17
|
EPHA4
|
|
18
|
FASN
|
|
19
|
FOXO3B
|
|
20
|
FRG2FP
|
|
21
|
GALNS
|
|
22
|
HBA2
|
|
23
|
HDAC10
|
|
24
|
INCENP
|
|
25
|
INTS1
|
|
26
|
KSR1
|
|
27
|
LIN28B
|
|
28
|
LINC01089
|
|
29
|
LRCH2
|
|
30
|
MAN2C1
|
|
31
|
MEG3
|
|
32
|
MEG8
|
|
33
|
MRGPRF
|
|
34
|
MRNIP
|
|
35
|
MSRA
|
|
36
|
NEB
|
|
37
|
NEFM
|
|
38
|
NOM1
|
|
39
|
NUP210
|
|
40
|
PBX1
|
|
41
|
PC
|
|
42
|
PCBP2-OT1
|
|
43
|
PCDH17
|
|
44
|
PLXNB1
|
|
45
|
PPP1R1B
|
|
46
|
PRRC2B
|
|
47
|
PTPRG-AS1
|
|
48
|
PYGO1
|
|
49
|
RNF130
|
|
50
|
RNF44
|
|
51
|
SBNO2
|
|
52
|
SCAMP4
|
|
53
|
SCARA3
|
|
54
|
SLC16A7
|
|
55
|
SLC29A2
|
|
56
|
SLITRK3
|
|
57
|
SYK
|
|
58
|
TAF15
|
|
59
|
TAF1C
|
|
60
|
TAF6L
|
|
61
|
TMEM271
|
|
62
|
TUBB3
|
|
63
|
VAX1
|
|
64
|
WDR17
|
|
65
|
WDR27
|
|
66
|
ZNF354C
|
|
67
|
ZNF414
|
|
68
|
ZNF667
|
|
69
|
ZNF667-AS1
|
|
70
|
ZNF675
|
|
71
|
ZNF730
|
|
72
|
ZNF736
|
[1] “Up in sensitivity:”
|
|
V1
|
|
1
|
ALX1
|
|
2
|
AMZ2
|
|
3
|
APOBEC3C
|
|
4
|
ARHGEF6
|
|
5
|
CD63
|
|
6
|
DCN
|
|
7
|
FAM72D
|
|
8
|
FAM92A
|
|
9
|
HIST1H1T
|
|
10
|
IL33
|
|
11
|
IRX3
|
|
12
|
LINC00326
|
|
13
|
LITAF
|
|
14
|
LYN
|
|
15
|
MRPS18C
|
|
16
|
NPIPA5
|
|
17
|
NRG1
|
|
18
|
PCSK6
|
|
19
|
PTGR1
|
|
20
|
PYCARD
|
|
21
|
RTN1
|
|
22
|
SP100
|
|
23
|
SSTR1
|
|
24
|
TMEM192
|
|
25
|
TSPAN5
|
|
26
|
YAF2
|
|
27
|
ZFAND1
|
|
28
|
ZNF277
|
[1] “TMZ” Removing transcripts with 100 th quantile < = 0 21188 transcripts will be tested [1] “Up in resistance:”
|
|
V1
|
|
1
|
CBR3
|
|
2
|
NRGN
|
|
3
|
RUNDC3B
|
|
4
|
TRIR
|
[1] “Up in sensitivity:”
|
|
V1
|
|
1
|
ADAM32
|
|
2
|
CCL2
|
|
3
|
LDLR
|
|
4
|
LINC02104
|
|
5
|
LUCAT1
|
|
6
|
NPIPB3
|
|
7
|
PRPF38B
|
|
8
|
RBM25
|
|
9
|
SNHG20
|
|
10
|
TPR
|
|
11
|
UTP3
|
[1] “Vin” Removing transcripts with 100 th quantile < = 0 20760 transcripts will be tested [1] “Up in resistance:”
[1] “Up in sensitivity:”
|
|
V1
|
|
1
|
CALD1
|
|
2
|
CTSL
|
|
3
|
DNAJB9
|
|
4
|
FLT1
|
|
5
|
FSTL1
|
|
6
|
S100A13
|
|
7
|
TRNP1
|
